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lambda-labs-gpu-cloud

Launch and manage Lambda Labs GPU cloud instances for ML training and inference.

Updated May 3, 2026
Search Tags:#mlops#ssh#distributed training#slurm#lambda labs#gpu cloud#llm fine-tuning
One-click install
npx skills add https://github.com/80portisfound/vibe-learning --skill lambda-labs-gpu-cloud-80portisfound
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lambda-labs-gpu-cloud
Source: https://github.com/80portisfound/vibe-learning/tree/main/packages/hermes/optional-skills/mlops/lambda-labs
Command: npx skills add https://github.com/80portisfound/vibe-learning --skill lambda-labs-gpu-cloud-80portisfound

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lambda-cloud-client, and includes references (resource) components.

What problem does it solve? Provisioning dedicated GPU infrastructure for ML training and inference is complex and expensive. This Skill guides you through launching, configuring, and managing Lambda Labs GPU instances and clusters so you can run training jobs without managing hardware. ## Core Features & Use Cases - Instance Lifecycle Management: Launch, monitor, and terminate GPU instances (B200, H100, A100, A10, and more) via console, Python API, or curl CLI. - Distributed Training Setup: Configure multi-GPU DDP/FSDP training and 16-512 GPU 1-Click Slurm clusters with InfiniBand networking. - Persistent Storage & Cost Control: Attach persistent filesystems for checkpoints and datasets, and select cost-effective GPU types per workload. - Use Case: You need to fine-tune a 7B LLM. Launch an 8x A100 instance with a persistent filesystem, SSH in, run distributed training with torchrun, and save checkpoints that survive instance termination. ## Quick Start Ask the assistant to launch a single H100 instance on Lambda Labs in us-west-1 with your SSH key and show you how to connect to it.

Frequently Asked Questions about lambda-labs-gpu-cloud

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I launch a GPU instance on Lambda Labs?▼

Launch a Lambda Labs GPU instance via the cloud console, the Python lambda-cloud-client API, or curl against the REST API. You must add an SSH key and payment method first, then select a GPU type, region, and optional filesystem.

Which Lambda Labs GPU should I use for LLM fine-tuning?▼

For fine-tuning a 7B model, a single A100 40GB works well; 70B models typically need 8x H100 instances. For inference or development, A10 or A6000 instances offer lower hourly costs.

Lambda Labs vs RunPod vs Vast.ai for GPU cloud?▼

Lambda Labs provides dedicated instances with pre-installed Lambda Stack, persistent filesystems, and no egress fees. RunPod offers cheaper spot and serverless options, while Vast.ai is a marketplace with the lowest prices but less consistency.

Does Lambda Labs support multi-node distributed training?▼

Yes, Lambda Labs supports multi-node training via torchrun across instances in the same region, and offers 1-Click Clusters with 16-512 H100 or B200 GPUs, Slurm scheduling, and 400 Gb/s InfiniBand interconnect.

Why is my Lambda Labs SSH connection refused?▼

SSH connection refused usually means the instance is still initializing; single-GPU instances take 3-5 minutes and multi-GPU instances 10-15 minutes. Also verify the instance status is active and your SSH key was added before launch.

What happens to my data when a Lambda Labs instance terminates?▼

The root volume is ephemeral, so all local data is lost on termination. Attach a persistent filesystem at launch time and store datasets, checkpoints, and outputs under /lambda/nfs/<filesystem-name> to preserve them.

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